Stepin, IliaAlonso Moral, José MaríaCatalá Bolós, AlejandroPereira Fariña, Martín2025-07-232025-07-232020I. Stepin, J. M. Alonso, A. Catala and M. Pereira-Fariña, "Generation and evaluation of factual and counterfactual explanations for decision trees and fuzzy rule-based classifiers," 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK, 2020, pp. 1-8, doi: 10.1109/FUZZ48607.2020.9177629.978-1-7281-6932-3https://hdl.handle.net/10347/42577Data-driven classification algorithms have proven highly effective in a range of complex tasks. However, their output is sometimes questioned, as the reasoning behind it may remain unclear due to a high number of poorly interpretable parameters used during training. Evidence-based (factual) explanations for single classifications answer the question why a particular class is selected in terms of the given observations. On the contrary, counterfactual explanations pay attention to why the rest of classes are not selected. Accordingly, we hypothesize that providing classifiers with a combination of both factual and counterfactual explanations is likely to make them more trustworthy. In order to investigate how such explanations can be produced, we introduce a new method to generate factual and counterfactual explanations for the output of pretrained decision trees and fuzzy rule-based classifiers. Experimental results show that unification of factual and counterfactual explanations under the paradigm of fuzzy inference systems proves promising for explaining the reasoning of classification algorithms.eng© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.book part10.1109/FUZZ48607.2020.9177629open access